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Automatic Counting and Classification of Microplastic Particles

Authors:
Javier Lorenzo-Navarro at University of Las Palmas de Gran Canaria
Javier Lorenzo-Navarro
Modesto Castrillón Santana at University of Las Palmas de Gran Canaria
Modesto Castrillón Santana
May Gómez at University of Las Palmas de Gran Canaria
May Gómez
Alicia Herrera at University of Las Palmas de Gran Canaria
Alicia Herrera
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Abstract and Figures

Microplastic particles have become an important ecological problem due to the huge amount of plastics debris that ends up in the sea. An additional impact is the ingestion of microplastics by marine species, and thus microplastics enter into the food chain with unpredictable effects on humans. In addition to the exploration of their presence in fishes, researchers are studying the presence of microplastics in coastal areas. The workload is therefore time consuming, due to the need to carry out regular campaigns to quantify their presence in the samples. So, in this work a method for automatic counting and classifying microplastic particles is presented. To the best of our knowledge, this is the first proposal to address this challenging problem. The method makes use of Computer Vision techniques for analyzing the acquired images of the samples; and Machine Learning techniques to develop accurate classifiers of the different types of microplastic particles that are considered. The obtained results show that making use of color based and shape based features along with a Random Forest classifier, an accuracy of 96.6% is achieved recognizing four types of particles: pellets, fragments, tar and line.
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... Therefore, the necessity of automated methods of interpretation of images, spectra, and chromatograms obtained from such instrumental methods are highly required. In 2018, Lorenzo-Navarro et al. (2018) came up with a machine learning-based novel approach with the objective of investigating an automated detection and classification method for microplastics. This method involves acquiring images using a high-definition scanner and the feature extraction of these images was carried out based on the colour, size, and other geometric characteristics of particles. ...
... The majority of imaging-related studies have used colour, size, and other geometrical features of polymer particles as features. In this case, Lorenzo-Navarro et al. (2018) have clearly explained the threshold method used to recognize polymer particles from the host matrix by assigning a threshold value for each pixel of the image. The same method has been employed by later studies (Bianco et al., 2020;Chaczko et al., 2018;Hufnagl et al., 2019;Massarelli et al., 2021). ...
... The list of machine learning techniques applicable in the classification of plastics is comprehensively reviewed by Yan et al. (2022). As reported by Lorenzo-Navarro et al. (2018), K Nearest-Neighbor, Adaptive Boosting, Random Forest, Support Vector Machine and C4.5 are the most prominent techniques which are applicable in microplastic research. This study found that the best classifier is the Random Forest (RDF) classifier, which offered an accuracy of 96.6% whereas other classifiers (mentioned in Table 4) have more than 90%, which still has more promising accuracy compared to manual classification methods (Lorenzo-Navarro et al. (2018). ...
A review on constructive classification framework of research trends in analytical instrumentation for secondary micro(nano)plastics: What is new and what needs next?
Article
  • Aug 2023
  • ENVIRON POLLUT
Secondary micro(nano)plastics generated from the degradation of plastics pose a major threat to environmental and human health. Amid the growing research on microplastics to date, the detection of secondary micro(nano)plastics is hampered by inadequate analytical instrumentation in terms of accuracy, validation, and repeatability. Given that, the current review provides a critical evaluation of the research trends in instrumental methods developed so far for the qualitative and quantitative determination of micro(nano)plastics with an emphasis on the evolution, new trends, missing links, and future directions. We conducted a meta-analysis of the growing literature surveying over 800 journal articles published from 2004 to 2022 based on the Web of Science database. The significance of this review is associated with the proposed novel classification framework to identify three main research trends, viz. (i) preliminary investigations, (ii) current progression, and (iii) novel advances in sampling, characterization, and quantification targeting both micro- and nano-sized plastics. Field Flow Fractionation (FFF) and Hydrodynamic Chromatography (HDC) were found to be the latest techniques for sampling and extraction of microplastics. Fluorescent Molecular Rotor (FMR) and Thermal Desorption-Proton Transfer Reaction-Mass Spectrometry (TD-PTR-MS) were recognized as the modern developments in the identification and quantification of polymer units in micro(nano)plastics. Powerful imaging techniques, viz. Digital Holographic Imaging (DHI) and Fluorescence Lifetime Imaging Microscopy (FLIM) offered nanoscale analysis of the surface topography of nanoplastics. Machine learning provided fast and less labor-intensive analytical protocols for accurate classification of plastic types in environmental samples. Although the existing analytical methods are justifiable merely for microplastics, they are not fully standardized for nanoplastics. Future research needs to be more inclined towards secondary nanoplastics for their effective and selective analysis targeting a broad range of environmental and biological matrices.
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... The use of a focal plane array (FPA)-based detection has improved FTIR imaging. This technique is unaffected by thickness and is unaffected by filter membranes or contaminants, making it an appropriate model for detecting microplastics [18,19,52,57,[112][113][114][115]. Plastics smaller than 20 μm can be detected and identified using FPA imaging, with 5-10 μm being a more acceptable limit. ...
... In Reflection mode, it has a spatial resolution of 5.5 μm and 1 μm in ATR mode. Before performing an FPA analysis, the material must first be purified and then concentrated in a filter [18,19,52,57,[112][113][114][115]. ...
A Review of Microplastic Identification and Characterization Methods in Aquatic Environments
Article
  • Feb 2024
Plastic waste has become a major global issue, with over 390.7 million tons of plastic produced in 2021. Because of its durability, low recycling rates, poor waste management, and maritime use, a considerable portion of plastic waste ends up in aquatic environments. Photo-oxidation and other mechanisms degrade plastics into microplastics (MPs), which are particles smaller than 5 mm. MPs can spread through the aerial, terrestrial, and aquatic areas, and running waterways serve as conduits for MP transport across various ecosystems. MPs have been found at various levels of the food web, and animals can ingest, inhale, or absorb them through their skin. MPs pose a significant health risk to flora and fauna, including marine creatures and humans, due to their small size, diverse colors, high abundance, and ability to adsorb antibiotic-resistant pathogens, causing cytotoxicity, acute reactions, undesirable immunological responses, neurotoxicity, and DNA alteration. MPs have a negative economic impact on industries such as agriculture, fishing, tourism, etc. Detecting and quantifying the presence of MPs is therefore critical. The purpose of this paper is to provide an overview of the various techniques and equipment used to detect and characterize MPs in aqueous environments. Identifying and educating the public about the primary sources of plastic pollution can help reduce the number of MPs in the environment.
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... However, despite this, these data are among the few that are an indicator of pollution in that area. Using a simple image analysis, a study by Lorenzo-Navarro et al. [81] was conducted, which applied the acquisition of an image with a flatbed scanner that, with the use of the so-called computer vision for the analysis of obtained images and machine learning, aimed to develop the classifiers of different types of MP particles. Here, the samples of pure MPs were to a good extent equivalent to the extraction of MPs from a natural seawater sample. ...
... The study using Mask R-CNN included a microplastic dataset including 3000 images, and was tested on 250 images with the precision over 93% [93]. The study by Lorenzo-Navaro et al. [81] used for training and testing 49 images of mixed microplastic samples and 20% of training samples vas used as validation with an average accuracy over 98%. However, such approach also pointed out limitations as (i) in monitoring microplastics, this is their visual identification/screening process which was a labor-intensive task that needs to be conducted by trained individuals and (ii) before classifying and counting MP particles, sample cleaning must be carried out to remove non-plastic material [45,93]. ...
Green Techniques for Detecting Microplastics in Marine with Emphasis on FTIR and NIR Spectroscopy—Short Review
Article
Full-text available
  • Aug 2023
The amount of microplastics (MPs) present in marine ecosystems are a growing concern, with potential impacts on human health because they are associated with an increase in the ecotoxicity of certain foods, such as fish. As a result, there has been a growing interest in developing effective methods for the analysis of MPs in marine waters. Traditional methods for MP analysis involve visual inspection and manual sorting, which can be time-consuming and subject to human error. However, novel methods have been developed that offer more efficient and accurate analyses. One such method is based on spectroscopy, such as Fourier transform infrared spectroscopy (FTIR). Another method involves the use of fluorescent dyes, which can selectively bind to microplastics and allow for their detection under UV light. Additionally, machine learning approaches have been developed to analyze large volumes of water samples for MP detection and classification. These methods involve the use of specialized algorithms that can identify and classify MPs based on their size, shape, and texture. Overall, these novel methods offer more efficient and accurate analyses of MPs in marine waters, which is essential for understanding the extent and impacts of MP pollution and for developing effective mitigation strategies. However, there is still a need for continued research and development to optimize these methods and improve their sensitivity and accuracy.
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... The slight visual differences between the background and MPs with various shapes and colors make the manual and automatic MPs' segmentation challenging, resulting in adverse effects on the analysis of the physical characteristics. A previous study (Lorenzo-Navarro et al., 2018) developed an adaptive threshold method to segment MPs from the scanned microscopic images with a simple background and a significant difference in gray values between the target and the background. Based on the geometric features of segmented results, the Random Forest technique was used to classify the shape of MPs (Lorenzo-Navarro et al., 2018). ...
... A previous study (Lorenzo-Navarro et al., 2018) developed an adaptive threshold method to segment MPs from the scanned microscopic images with a simple background and a significant difference in gray values between the target and the background. Based on the geometric features of segmented results, the Random Forest technique was used to classify the shape of MPs (Lorenzo-Navarro et al., 2018). In addition to standard optical microscopic images, Fourier transforms infrared spectroscopic (FTIR) images captured by FTIR microscope with focal plane array (FPA) was also used to segment and identify the polymer of MPs. ...
Proceeding the categorization of microplastics through deep learning-based image segmentation
Article
  • Jul 2023
  • SCI TOTAL ENVIRON
Microplastics (MPs) have been recognized as prominent anthropogenic pollutants that inflict significant harm to marine ecosystems. Various approaches have been proposed to mitigate the risks posed by MPs. Gaining an understanding of the morphology of plastic particles can provide valuable insights into the source and their interaction with marine organisms, which can assist the development of response measures. In this study, we present an automated technique for identifying MPs through segmentation of MPs in microscopic images using a deep convolutional neural network (DCNN) based on a shape classification nomenclature framework. We used MP images from diverse samples to train a Mask Region Convolutional Neural Network (Mask R-CNN) based model for classification. Erosion and dilation operations were added to the model to improve segmentation results. On the testing dataset, the mean F1-score (F1) of segmentation and shape classification was 0.7601 and 0.617, respectively. These results demonstrate the potential of proposed method for the automatic segmentation and shape classification of MPs. Furthermore, by adopting a specific nomenclature, our approach represents a practical step towards the global standardization of MPs categorization criteria. This work also identifies future research directions to improve accuracy and further explore the possibilities of using DCNN for MPs identification.
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... The slight visual differences between the background and MPs with various shapes and colors make the manual and automatic MPs' segmentation challenging, resulting in adverse effects on the analysis of the physical characteristics. A previous study (Lorenzo-Navarro et al., 2018) developed an adaptive threshold method to segment MPs from the scanned microscopic images with a simple background and a significant difference in gray values between the target and the background. Based on the geometric features of segmented results, the Random Forest technique was used to classify the shape of MPs (Lorenzo-Navarro et al., 2018). ...
... A previous study (Lorenzo-Navarro et al., 2018) developed an adaptive threshold method to segment MPs from the scanned microscopic images with a simple background and a significant difference in gray values between the target and the background. Based on the geometric features of segmented results, the Random Forest technique was used to classify the shape of MPs (Lorenzo-Navarro et al., 2018). In addition to standard optical microscopic images, Fourier transforms infrared spectroscopic (FTIR) images captured by FTIR microscope with focal plane array (FPA) was also used to segment and identify the polymer of MPs. ...
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... Typically, this task is considered as an independent task, 76 but it is sometimes considered as a follow-up task after segmentation. 87, 104 In most instances, a DL-CNN architecture is applied to extract and learn relevant features related to size, shape, color, texture, and other visual cues through the labeled images of MPs ( Figure 5). Usually, the DL-CNN consists of two main functional modules: the feature-extraction module and the classification module. ...
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... The methods currently used in image-related research that are speedy, reliable, repeatable, and highly efficient are those based on computer vision and deep learning. Several studies have employed machine learning and computer vision in analysis to quantify and categorize microplastics [17][18][19][20] . The results have indicated a highly accurate classification. ...
A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light
Article
Full-text available
  • Feb 2024
Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a laboratory. However, deep learning can assist in identifying and quantifying microplastics from an image. This paper presents a novel microplastic classification method that combines the benefits of UV light with deep learning. The Faster-RCNN model with a ResNet-50-FPN backbone was implemented to detect and identify microplastics. Microplastic images from the field taken under UV light were used to train and validate the model. This classification model achieved a high precision of 85.5–87.8%, and the mAP scores were 33.9% on an internal test set and 35.7% on an external test set. This classification approach provides a high-accuracy, low-cost, and time-effective automated identification and counting of microplastics.
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... The nature and original form of MPs may be identified based on their shape. Fragments are small plastics resulting from the degradation of larger products whereas line or fiber are elongated, thin, and/or fibrous materials (Lorenzo-Navarro et al. 2018;Tanchuling and Osorio 2020a). Possible sources of fragments in the Pasig River are containers, packaging materials, and other debris that have degraded with time. ...
Characterization and Quantification of Surficial Sediment Microplastics and Its Correlation with Heavy Metals, Soil Texture, and Flow Velocity
Article
Investigating the presence of microplastics in the Pasig River, the highest plastic-emitting river in the Philippines and one of the ocean’s leading contributors of these materials, is highly relevant. In this study, surficial sediments in eight stations along the main channel of the Pasig River were analyzed for microplastic content, heavy metals, and soil texture. The microplastics were categorized according to shape, color, and size. The polymer type was determined using infrared spectroscopy. The number of microplastics was correlated with heavy metals, soil texture, and flow velocity. The results show that the dominant shape, color, size, and polymer type were fragment, color white, size range from 0 to 500 μm, and polyvinyl alcohol, respectively. Downstream stations 5 and 8 had the highest microplastic concentration. The number of microplastics showed a positive moderate correlation with percent sand (r = 0.40) and clay (r = 0.42), and a negative high correlation with percent silt (r =-0.80). For microplastics and heavy metals, a positive moderate correlation was observed for iron (r = 0.52) and zinc (r = 0.58), while a negative weak correlation was present for lead (r = -0.06). Microplastics and flow velocity showed weak correlations demonstrating that the accumulation in the bottom river sediment may be based on the proximity of the plastic source rather than on the flowing water with plastic sources. Results reflect the diverse influences on the Pasig River which is surrounded by informal settlements, manufacturing industries, urban offices, and residential areas. More plastic management initiatives and better implementation of existing laws should be done by the government.
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... For detailed information, there are the most cited authors based on some important indicators, containing Total Publications, Total Cited, Links, and Total Link Strength by setting the threshold of 2 in Figure 15. Based on the results of Figure 15, in these involved authors Ferraro, Pietro, Bianco, and Vittorio are the most active through 6 publications and 41 citations from the same institute called Consiglio Nazionale delle Ricerche (CNR).Ferraro, Pietro and Bianco, Vittorio focused on researching the microplastics including microplastics identification, classifying and automatic detection via holographic imaging and machine Learning[45][46][47]. ...
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Article
Full-text available
Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019–2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.
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Explainable Detection of Microplastics Using Transformer Neural Networks
Chapter
  • Dec 2022
  • Lect Notes Comput Sci
Microplastics are environmental contaminants that put marine and aquatic ecosystems at serious risk. Monitoring microplastics is necessary to understand the level of microplastic pollution in our environment. However, the lack of a standard protocol for quantifying and classifying microplastics causes problems in the reliability and comparability of results. Previous literature has employed deep learning models to classify and quantify microplastic polymers with great success, but the ability of these models to classify microplastics from new domains is unanswered. This paper presents an innovative approach to microplastic classification that employs a deep learning approach using a transformer neural network. Our specific contributions are: (1) A novel way to pre-process FTIR spectral data to dramatically increase classification accuracy. (2) Developed a transformer neural network for classifying microplastic polymer FTIR spectra. With the inclusion of a wider range of data, future deep learning approaches will improve the classification and quantification of microplastic polymers, subsequently reducing the costs and labour involved.
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Microplastic and tar pollution on three Canary Islands beaches: An annual study
Article
  • Nov 2017
  • MAR POLLUT BULL
Marine debris accumulation was analyzed from three exposed beaches of the Canary Islands (Lambra, Famara and Las Canteras). Large microplastics (1-5mm), mesoplastics (5-25mm) and tar pollution were assessed twice a month for a year. There was great spatial and temporal variability in the Canary Island coastal pollution. Seasonal patterns differed at each location, marine debris concentration depended mainly of local-scale wind and wave conditions. The most polluted beach was Lambra, a remote beach infrequently visited. The types of debris found were mainly preproduction resin pellets, plastic fragments and tar, evidencing that pollution was not of local origin, but it cames from the open sea. The levels of pollution were similar to those of highly industrialized and contaminated regions. This study corroborates that the Canary Islands are an area of accumulation of microplastics and tar rafted from the North Atlantic Ocean by the southward flowing Canary Current.
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Imagenet classification with deep convolutional neural networks
Conference Paper
  • Jan 2012
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
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A standardized method for sampling and extraction methods for quantifying microplastics in beach sand
Article
  • Sep 2016
Microplastics are ubiquitous in the environment, are frequently ingested by organisms, and may potentially cause harm. A range of studies have found significant levels of microplastics in beach sand. However, there is a considerable amount of methodological variability among these studies. Methodological variation currently limits comparisons as there is no standard procedure for sampling or extraction of microplastics. We identify key sampling and extraction procedures across the literature through a detailed review. We find that sampling depth, sampling location, number of repeat extractions, and settling times are the critical parameters of variation. Next, using a case-study we determine whether and to what extent these differences impact study outcomes. By investigating the common practices identified in the literature with the case-study, we provide a standard operating procedure for sampling and extracting microplastics from beach sand.
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Automatic analysis of zooplankton using digitized images: State of the art and perspectives for Latin America
Article
  • Jan 2013
The study of zooplankton communities is very important in the context of the biology, ecology, and conservation of the marine ecosystem. However, research on zooplankton implies a highly costly process in terms of time and effort, requiring trained personnel for identification, counting and measuring of various taxonomic groups, including biomass estimates. At present, there is an increasing interest for implementing new techniques allowing automatic analyses of zooplankton by means of informatics technology and assessment of digitized images. In this work, the state of the art of some methodologies applied to digital images of zooplankton is revised describing the general methods used, such as ZooScan-ZooProcess and ZooImage, and an analysis of their results for identification, a comparison with traditional methods, and recommendations to improve the identification process. Additionally, a brief summary of the experiences with these systems is presented, focused on the analysis of spatial and temporal variability of the zooplankton community by means of its size spectrum and tests for deriving biomass estimates. Finally, a list of capacities and limitations of the methods is discussed using recent literature, as well as the perspectives of development and applications of this new technology can offer to the scientific community of the Latin American region.
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Microplastics in the Ocean
Article
  • Sep 2015
  • ARCH ENVIRON CON TOX
Since their ubiquity in the ocean and marine organisms was first revealed, global concern about microplastics has grown considerably. The North Pacific Ocean and the adjacent marginal seas have high levels of microplastic contamination compared with the global average. This special issue on microplastics was organized by the North Pacific Marine Science Organization to share information on microplastic pollution in the North Pacific region. The special issue highlights high levels of contamination in the North Pacific both on shorelines and at the sea surface. Particularly high levels of contamination were reported on the western and southern coasts of Korea. Sources, including sewage discharge, aquaculture, and shipyards, were implicated. With the direction and energy of surface winds and currents have an important influence on shoreline patterns of distribution. The special issue also demonstrates potential for ingestion of microplastic by small planktonic organisms at the base of the food chain. A wide range of chemicals are associated with plastic debris and concerns are expressed about the potential for these chemicals to transfer to biota upon ingestion. As an introduction to the topic, this paper provides a brief background on microplastic contamination, highlights some key research gaps, and summarizes findings from the articles published in this issue.
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Microplastics in sediments: A review of techniques, occurrence and effects
Article
  • Jun 2015
Microplastics are omnipresent in the marine environment and sediments are hypothesized to be major sinks of these plastics. Here, over 100 articles spanning the last 50 year are reviewed with following objectives: (i) to evaluate current microplastic extraction techniques, (ii) to discuss the occurrence and worldwide distribution of microplastics in sediments, and (iii) to make a comprehensive assessment of the possible adverse effects of this type of pollution to marine organisms. Based on this review we propose future research needs and conclude that there is a clear need for a standardized techniques, unified reporting units and more realistic effect assessments. Copyright © 2015 Elsevier Ltd. All rights reserved.
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